The importance of mutual monitoring in recommender systems based on learning agents derives from the consideration that a learning agent needs to interact with other agents in its environment in order to Improve its individual performances. In this paper we present a novel framework, called EVA, that introduces a strategy to improve the performances of recommender agents based on a dynamic computation of the agent's reputation. Some preliminary experiments on real users show that our approach, implemented on the top of some well-known recommender systems, introduces significant improvements in terms of effectiveness.

Dynamically Computing Reputation of Recommender Agents with Learning Capabilities

ROSACI D;SARNE' G
2008

Abstract

The importance of mutual monitoring in recommender systems based on learning agents derives from the consideration that a learning agent needs to interact with other agents in its environment in order to Improve its individual performances. In this paper we present a novel framework, called EVA, that introduces a strategy to improve the performances of recommender agents based on a dynamic computation of the agent's reputation. Some preliminary experiments on real users show that our approach, implemented on the top of some well-known recommender systems, introduces significant improvements in terms of effectiveness.
978-354085256-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/14007
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